Filler T J, Rickert C H, Zhao B
Westfälische Wilhelms-Universität, Institute of Anatomy, Münster, Germany.
Comput Methods Programs Biomed. 1995 Jul;47(2):177-82. doi: 10.1016/0169-2607(95)01649-e.
This paper introduces a program written on the image analysis system VIDAS 2.5. It enables the automatic quantification of high numbers of adhesion areas of vital human platelets, thus allowing statistical analysis. These adhesion areas were observed by reflection contrast microscopy (RCM), which generates images of an intense contrast and serves as a prerequisite for an evaluation by image analysis. However, RCM-photographs of the observed platelets have highly varying mean greyvalues and greyranges. These common problems for self-operating identification are excluded by two procedures within the program: 1. calibration of the scanning process for an optimal use of the available greyvalues provided by the negative, camera, and the image analysis system; and 2. relation of the threshold for discrimination of adhesion areas to the statistic parameters of the histogram within each individual digitized image. Images processed according to these prerequisites were transferred to the VIDAS implemented routines for identification and measurement of areas. Thus, image analysis combined with RCM offers a tool for basic and clinical platelet research, which is shown by an example of stimulation and inhibited stimulation of platelet activation.
本文介绍了一个在图像分析系统VIDAS 2.5上编写的程序。它能够自动定量大量活的人体血小板的粘附面积，从而进行统计分析。这些粘附面积通过反射对比显微镜（RCM）进行观察，RCM能生成高对比度的图像，这是通过图像分析进行评估的前提条件。然而，观察到的血小板的RCM照片具有高度变化的平均灰度值和灰度范围。该程序中的两个步骤排除了自动识别中的这些常见问题：1. 校准扫描过程，以便最佳利用底片、相机和图像分析系统提供的可用灰度值；2. 将粘附面积识别阈值与每个数字化图像内直方图的统计参数相关联。根据这些前提条件处理的图像被传输到VIDAS中用于识别和测量面积的已实现例程。因此，图像分析与RCM相结合为基础和临床血小板研究提供了一种工具，血小板活化的刺激和抑制刺激示例展示了这一点。
